Our work considers a fast method for finding a "best" test for a nominal attribute for generating a binary decision tree. In this regard, we note that when a nominal attribute has n distinct values, the prior art requires computing impurity functions on each of (2.sup.n-1 -1) possible partitions of the n values into two subsets, in general, and finding the optimum case among them. Especially from a vantage point of the present invention, as described below, it may be discerned that this prior art approach can introduce computational complexities which may make it impractical for finding best or near best tests for many real data mining applications in which a binary decision tree is used as a classification model.